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import json
import os
import random
import string
import time
from collections import defaultdict
from dotenv import load_dotenv
from openai import OpenAI
from api.llm import LLMManager
from utils.config import Config
from resources.data import fixed_messages, topic_lists
from resources.prompts import prompts
from tests.testing_prompts import candidate_prompt
config = Config()
load_dotenv()
def complete_interview(interview_type, exp_name, requirements="", difficulty="", topic="", model="gpt-3.5-turbo"):
client = OpenAI(base_url="https://api.openai.com/v1")
llm = LLMManager(config, prompts)
llm_name = config.llm.name
print(f"Starting evaluation interviewer LLM: {llm_name}, candidate_LLM: {model} interview_type: {interview_type}")
# Select a random topic or difficulty if not provided
topic = topic or random.choice(topic_lists[interview_type])
difficulty = difficulty or random.choice(["easy", "medium", "hard"])
problem_statement_text = llm.get_problem_full(requirements, difficulty, topic, interview_type)
interview_data = defaultdict(
lambda: None,
{
"interviewer_llm": llm_name,
"candidate_llm": model,
"inputs": {
"interview_type": interview_type,
"difficulty": difficulty,
"topic": topic,
"requirements": requirements,
},
"problem_statement": problem_statement_text,
"transcript": [],
"feedback": None,
"average_response_time_seconds": 0,
},
)
# Initialize interviewer and candidate messages
messages_interviewer = llm.init_bot(problem_statement_text, interview_type)
chat_display = [[None, fixed_messages["start"]]]
messages_candidate = [
{"role": "system", "content": candidate_prompt},
{"role": "user", "content": f"Your problem: {problem_statement_text}"},
{"role": "user", "content": chat_display[-1][1]},
]
response_times = []
previous_code = ""
for _ in range(30):
response = client.chat.completions.create(
model=model, messages=messages_candidate, temperature=1, response_format={"type": "json_object"}
)
response_json = json.loads(response.choices[0].message.content)
code = response_json.get("code", "")
candidate_message = response_json.get("message", "")
if not code and not candidate_message:
print("No message or code in response")
continue
messages_candidate.append({"role": "assistant", "content": response.choices[0].message.content})
if code:
interview_data["transcript"].append(f"CANDIDATE CODE: {code}")
elif candidate_message:
interview_data["transcript"].append(f"CANDIDATE MESSAGE: {candidate_message}")
chat_display.append([candidate_message, None])
# Check if the interview should finish
if response_json.get("finished") and not response_json.get("question"):
break
send_time = time.time()
messages_interviewer, chat_display, previous_code = llm.send_request_full(code, previous_code, messages_interviewer, chat_display)
response_times.append(time.time() - send_time)
messages_candidate.append({"role": "user", "content": chat_display[-1][1]})
message_split = messages_interviewer[-1]["content"].split("#NOTES#")
interviewer_answer = message_split[0]
interview_data["transcript"].append(f"INTERVIEWER MESSAGE: {interviewer_answer}")
if len(message_split) > 1:
interviewer_note = message_split[1]
interview_data["transcript"].append(f"INTERVIEWER HIDDEN NOTE: {interviewer_note}")
interview_data["feedback"] = llm.end_interview_full(problem_statement_text, messages_interviewer, interview_type)
interview_data["average_response_time_seconds"] = round(sum(response_times) / len(response_times), 2) if response_times else 0
current_time = time.strftime("%Y%m%d-%H%M%S")
random_suffix = "".join(random.choices(string.ascii_letters + string.digits, k=10))
file_path = os.path.join("records", exp_name, f"{current_time}-{random_suffix}.json")
os.makedirs(os.path.dirname(file_path), exist_ok=True)
with open(file_path, "w") as file:
json.dump(interview_data, file, indent=4)
return file_path, interview_data
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